Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

[Paper] [Code]

Planning safe trajectories for nonlinear dynamical systems subject to model uncertainty and disturbances is challenging. In this work, we present a novel approach to tackle chance-constrained trajectory planning problems with nonconvex constraints, whereby obstacle avoidance chance constraints are reformulated using the signed distance function. We propose a novel sequential convex programming algorithm and prove that under a discrete time problem formulation, it is guaranteed to converge to a solution satisfying first-order optimality conditions. We demonstrate the approach on an uncertain 6 degrees of freedom spacecraft system and show that the solutions satisfy a given set of chance constraints.

Bibtex

@inproceedings{ccscp_2020,
	title={Chance-Constrained Optimal Altitude Control of a Rocket},
	author={Lew, T. and Bonalli, R. and Pavone, M.},
	booktitle={European Control Conference},
	year = {2020},
}